code: def build_toy_dataset(N, D, K, sigma=1):
      x_train = np.zeros((D, N))
      w = np.random.normal(0.0, 2.0, size=(D, K))
      z = np.random.normal(0.0, 1.0, size=(K, N))
      mean = np.dot(w, z)
      for d in range(D):
          for n in range(N):
              x_train[d, n] = np.random.normal(mean[d, n], sigma)
  
      print("True principal axes:")
      print(w)
      return x_train
  
file_path: edward/examples/probabilistic_pca_subsampling.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import edward
  import numpy
  import tensorflow
  from edward.models import Normal
label_desc: Use image node_id 1 for calling a global function with example usage: build_toy_dataset(N, D, K, sigma) and returns: x_train